




'''simple docstring'''







import argparse
import json
from typing import List

from ltp import LTP

from transformers import BertTokenizer







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   if (
       (cp >= 0x4_e00 and cp <= 0x9_fff)
       or (cp >= 0x3_400 and cp <= 0x4_dbf)  #
       or (cp >= 0x20_000 and cp <= 0x2a_6df)  #
       or (cp >= 0x2a_700 and cp <= 0x2b_73f)  #
       or (cp >= 0x2b_740 and cp <= 0x2b_81f)  #
       or (cp >= 0x2b_820 and cp <= 0x2c_eaf)  #
       or (cp >= 0xf_900 and cp <= 0xf_aff)
       or (cp >= 0x2f_800 and cp <= 0x2f_a1f)  #
   ):  #
      return True

   return False







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   for char in word:
      A__       =     ord(UpperCAmelCase						)
      if not _is_chinese_char(UpperCAmelCase						):
         return 0
   return 1







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     set()

   for token in tokens:
      A__       =     len(UpperCAmelCase						) > 1 and is_chinese(UpperCAmelCase						)
      if chinese_word:
         word_set.add(UpperCAmelCase						)
   A__       =     list(UpperCAmelCase						)
   return word_list







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   if not chinese_word_set:
      return bert_tokens
   A__       =     max([len(UpperCAmelCase						) for w in chinese_word_set]						)

   A__       =     bert_tokens
   A__  ,			A__       =     0, len(UpperCAmelCase						)
   while start < end:
      A__       =     True
      if is_chinese(bert_word[start]						):
         A__       =     min(end - start      ,UpperCAmelCase						)
         for i in range(UpperCAmelCase      ,1      ,-1						):
            A__       =     ''.join(bert_word[start : start + i]						)
            if whole_word in chinese_word_set:
               for j in range(start + 1      ,start + i						):
                  A__       =     '##' + bert_word[j]
               A__       =     start + i
               A__       =     False
               break
      if single_word:
         start += 1
   return bert_word







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     []

   for i in range(0      ,len(UpperCAmelCase						)      ,100						):
      A__       =     ltp_tokenizer.seg(lines[i : i + 100]						)[0]
      A__       =     [get_chinese_word(UpperCAmelCase						) for r in res]
      ltp_res.extend(UpperCAmelCase						)
   assert len(UpperCAmelCase						) == len(UpperCAmelCase						)

   A__       =     []
   for i in range(0      ,len(UpperCAmelCase						)      ,100						):
      A__       =     bert_tokenizer(lines[i : i + 100]      ,add_special_tokens=UpperCAmelCase      ,truncation=UpperCAmelCase      ,max_length=512						)
      bert_res.extend(res['input_ids']						)
   assert len(UpperCAmelCase						) == len(UpperCAmelCase						)

   A__       =     []
   for input_ids, chinese_word in zip(UpperCAmelCase      ,UpperCAmelCase						):
      A__       =     []
      for id in input_ids:
         A__       =     bert_tokenizer._convert_id_to_token(UpperCAmelCase						)
         input_tokens.append(UpperCAmelCase						)
      A__       =     add_sub_symbol(UpperCAmelCase      ,UpperCAmelCase						)
      A__       =     []
      # We only save pos of chinese subwords start with ##, which mean is part of a whole word.
      for i, token in enumerate(UpperCAmelCase						):
         if token[:2] == "##":
            A__       =     token[2:]
            # save chinese tokens' pos
            if len(UpperCAmelCase						) == 1 and _is_chinese_char(ord(UpperCAmelCase						)						):
               ref_id.append(UpperCAmelCase						)
      ref_ids.append(UpperCAmelCase						)

   assert len(UpperCAmelCase						) == len(UpperCAmelCase						)

   return ref_ids







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   with open(args.file_name      ,'r'      ,encoding='utf-8'						) as f:
      A__       =     f.readlines()
   A__       =     [line.strip() for line in data if len(UpperCAmelCase						) > 0 and not line.isspace()]  # avoid delimiter like '\u2029'
   A__       =     LTP(args.ltp						)  # faster in GPU device
   A__       =     BertTokenizer.from_pretrained(args.bert						)

   A__       =     prepare_ref(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						)

   with open(args.save_path      ,'w'      ,encoding='utf-8'						) as f:
      A__       =     [json.dumps(UpperCAmelCase						) + '\n' for ref in ref_ids]
      f.writelines(UpperCAmelCase						)


if __name__ == "__main__":
    lowerCAmelCase_     						=				argparse.ArgumentParser(description='''prepare_chinese_ref''')
    parser.add_argument(
        '''--file_name''',
        type=str,
        default='''./resources/chinese-demo.txt''',
        help='''file need process, same as training data in lm''',
    )
    parser.add_argument(
        '''--ltp''', type=str, default='''./resources/ltp''', help='''resources for LTP tokenizer, usually a path'''
    )
    parser.add_argument('''--bert''', type=str, default='''./resources/robert''', help='''resources for Bert tokenizer''')
    parser.add_argument('''--save_path''', type=str, default='''./resources/ref.txt''', help='''path to save res''')

    lowerCAmelCase_     						=				parser.parse_args()
    main(args)









"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor

@require_torch
@require_vision
class 						lowercase   (   unittest.TestCase   ):





      @slow
      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            lowerCAmelCase    		=			AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            model.to(lowercase					)

            from datasets import load_dataset

            lowerCAmelCase    		=			load_dataset("""nielsr/rvlcdip-demo"""					)

            lowerCAmelCase    		=			dataset["""train"""][0]["""image"""].convert("""RGB"""					)

            lowerCAmelCase    		=			image_processor(lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)
                  lowerCAmelCase    		=			outputs.logits

            lowerCAmelCase    		=			torch.Size((1, 16)					)
            self.assertEqual(logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor(
                [-0.4_158, -0.4_092, -0.4_347] ,		device=lowercase ,		dtype=torch.float ,		)
            self.assertTrue(torch.allclose(logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






